Abstract
When predicting a numeric outcome, some measure of accuracy is typically used to evaluate the model’s effectiveness. However, there are different ways to measure accuracy, each with its own nuance. In Section 5.1 we define common measures for evaluating quantitative performance. We also discuss the concept of variance-bias trade-off (Section 5.2), and the implication of this principle for predictive modeling. In Section 5.3, we demonstrate how measures of predictive performance can be generated in R.
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References
Kvålseth T (1985). “Cautionary Note About R 2.” American Statistician, 39(4), 279–285.
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© 2013 Springer Science+Business Media New York
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Kuhn, M., Johnson, K. (2013). Measuring Performance in Regression Models. In: Applied Predictive Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6849-3_5
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DOI: https://doi.org/10.1007/978-1-4614-6849-3_5
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6848-6
Online ISBN: 978-1-4614-6849-3
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